Introduction

Hello! my name is Sophia, I am a molecular biologist working in proteomics, currently a Postdoctoral fellow. Before Data School, I coded on Macro (Excel). I used to spend a lot of time cleaning and tidying protein data, I always felt like I could do it faster if I had programming skills. These weeks learning R have changed my daily work. The possible things that we could do have opened my mind to a new perspective of my research.

My Project

I am working in the study of ryegrass, a potential source for gluten peptides contamination. Gluten refers to a class of storage proteins found in cereal grains, including wheat, rye, barley, and oats. Consumption of these gluten proteins leads to an autoimmune response in the case of coeliac disease. Previous studies have identified gluten-like proteins in ryegrass. Since this is a common weed found in grain fields, there is a possibility of cross-contamination. First of all, I need to characterize the gluten proteins and peptides with ryegrass origen. To do so I have performed Data-dependent mass spectrometric analysis. The results of this study provided identification of 3162 proteins, and 8231 peptides. Now what I need to do is found how many of them are gluten-like.

Preliminary results

I will analyse the amino acids composition of the proteins of my database. Since gluten proteins have a high composition of the amino acids Glutamine (Q) and Proline (P). I will search for all proteins that have over 20% Glutamine.

Tables

Table 1: Protein Database

N	Accession	Name	Sequence
1	spP4910614331_MAIZE	14-3-3-like	MASAELSREENVYMAKLAEQAERYEEMVEFMEKVAKTVDSEELTVEERNLLSVAYKNVIGARRASWRIISSIEQKEEGRGNEDRVTLIKDYRGKIETELTKICDGILKLLETHLVPSSTAPESKVFYLKMKGDYYRYLAEFKTGAERKDAAENTMVAYKAAQDIALAELAPTHPIRLGLALNFSVFYYEILNSPDRACSLAKQAFDEAISELDTLSEESYKDSTLIMQLLRDNLTLWTSDISEDPAEEIREAPKRDSSEGQ
2	spQ84Q72HS181_ORYSJ	18.1	MSLIRRSNVFDPFSLDLWDPFDGFPFGSGSRSSGSIFPSFPRGTSSETAAFAGARIDWKETPEAHVFKADVPGLKKEEVKVEVEDGNVLQISGERSKEQEEKTDKWHRVERSSGKFLRRFRLPENTKPEQIKASMENGVLTVTVPKEEPKKPDVKSIQVTG
3	spP69555PSBH_WHEAT	Photosystem	MATQTVEDSSKPRPKRTGAGSLLKPLNSEYGKVAPGWGTTPFMGVAMALFAIFLSIILEIYNSSVLLDGILTN
4	spP36886PSAK_HORVU	Photosystem	MASQLSAMTSVPQFHGLRTYSSPRSMATLPSLRRRRSQGIRCDYIGSSTNLIMVTTTTLMLFAGRFGLAPSANRKATAGLKLEARESGLQTGDPAGFTLADTLACGAVGHIMGVGIVLGLKNTGVLDQIIG
5	spQ6YZE2GSA_ORYSJ	Glutamate-1-semialdehyde	MAGAAAASAAAAAVASGISARPVAPRPSPSRARAPRSVVRAAISVEKGEKAYTVEKSEEIFNAAKELMPGGVNSPVRAFKSVGGQPIVFDSVKGSRMWDVDGNEYIDYVGSWGPAIIGHADDTVNAALIETLKKGTSFGAPCVLENVLAEMVISAVPSIEMVRFVNSGTEACMGALRLVRAFTGREKILKFEGCYHGHADSFLVKAGSGVATLGLPDSPGVPKGATSETLTAPYNDVEAVKKLFEENKGQIAAVFLEPVVGNAGFIPPQPGFLNALRDLTKQDGALLVFDEVMTGFRLAYGGAQEYFGITPDVSTLGKIIGGGLPVGAYGGRKDIMEMVAPAGPMYQAGTLSGNPLAMTAGIHTLKRLMEPGTYDYLDKITGDLVRGVLDAGAKTGHEMCGGHIRGMFGFFFTAGPVHNFGDAKKSDTAKFGRFYRGMLEEGVYLAPSQFEAGFTSLAHTSQDIEKTVEAAAKVLRRI
Note: 5 examples of proteins found in the database. The column Sequence indicates the amino acids (letter code) that make up each protein.

Look for amino acids Q and P.

library(tidyverse)
library(Biostrings)
my_database_comp <- mutate(my_database, totalAA= str_count(Sequence, '\\w'), Qcomp= str_count(Sequence,'Q')) %>% 
  mutate(Q100 =  Qcomp *100 / totalAA) %>% 
  mutate(Pcomp= str_count(Sequence,'P')) %>% 
  mutate(P100= Pcomp *100/totalAA)

my_database_comp[,'Q100']=round(my_database_comp[,'Q100'],2)
my_database_comp[,'P100']=round(my_database_comp[,'P100'],2)

Table 2: Aminoacid composition

N	Accession	Name	totalAA	Qcomp	Q100	Pcomp	P100
1	spP4910614331_MAIZE	14-3-3-like	261	6	2.30	7	2.68
2	spQ84Q72HS181_ORYSJ	18.1	161	4	2.48	12	7.45
3	spP69555PSBH_WHEAT	Photosystem	73	1	1.37	5	6.85
4	spP36886PSAK_HORVU	Photosystem	131	5	3.82	5	3.82
5	spQ6YZE2GSA_ORYSJ	Glutamate-1-semialdehyde	478	8	1.67	27	5.65
Note: totalAA = Number of total amino acids of the protein Qcomp= Number of Glutamine found in the protein Q100= Porcentage of Glutamine in the protein Pcomp= Number of Proline found in the protein P100= Porcentage of Proline in the protein

Working with Protein Data

Plotting

Figure 1: Glutamine and Proline composition in Ryegrass

Figure 2: Glutamine and Proline composition in Ryegrass

Figure 3: Glutamine and Proline composition in Ryegrass

Your figure and table captions are automatically numbered and can be referenced in the text if needed: see eg. Table 1 and Figure ??

My Digital Toolbox

To work with Protein Databases, that are usually in the format .fasta. I have used the package Biostrings. For tyding the data,Tidyverse (my new best friend) and plyr.

What digital tools have you been using in your project? Do you expect that everything will be able to be completed within R, or will you need to work with multiple tools to get the right result? Which of the digital skills needed for your project have you learned since starting Data School?

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• R - dplyr, ggplot, …
• Python
• SQL

My time went …

What parts of your project take the most time and effort? Were there any surprising challenges you encountered, and how did you solve them?

Next steps

What further steps do you wish your project could take? Or are there any new digital skills that you are keen to develop as a result of your involvement in the Data School?

My Data School Experience

This summary is mostly about your project. However we would also like to hear about other parts of your Data School experience. What aspects of the program did you really enjoy? Have you tried applying the skills you have learned in your daily work? Have you been able to transfer this knowledge to your team members? Any descriptions of the personal impact the program has had are welcome here as well!